Speech recognition using word-in-phrase command

ABSTRACT

A method is described that corrects incorrect text associated with recognition errors in computer-implemented speech recognition. The method includes the step of performing speech recognition on an utterance to produce a recognition result for the utterance. The command includes a word and a phrase. The method includes determining if a word closely corresponds to a portion of the phrase. A speech recognition result is produced if the word closely corresponds to a portion of the phrase.

TECHNICAL FIELD

This invention relates to computer-implemented speech recognition, andmore particularly, to speech recognition using a word-in-phrase command.

BACKGROUND

A speech recognition system analyzes a user's speech to determine whatthe user said. Most speech recognition systems are frame-based. In aframe-based system, a processor divides a signal descriptive of thespeech to be recognized into a series of digital frames, each of whichcorresponds to a small time increment of the speech.

A speech recognition system may be a “discrete” system that recognizesdiscrete words or phrases but which requires the user to pause brieflybetween each discrete word or phrase. Alternatively, a speechrecognition system may be a “continuous” system that can recognizespoken words or phrases regardless of whether the user pauses betweenthem. Continuous speech recognition systems typically have a higherincidence of recognition errors in comparison to discrete recognitionsystems due to complexities of recognizing continuous speech.

In general, the processor of a continuous speech recognition systemanalyzes “utterances” of speech. An utterance includes a variable numberof frames and corresponds, for example, to a period of speech followedby a pause of at least a predetermined duration.

The processor determines what the user said by finding acoustic modelsthat best match the digital frames of an utterance, and by identifyingtext that corresponds to those acoustic models. An acoustic model maycorrespond to a word, phrase or command from a vocabulary. An acousticmodel also may represent a sound, or phoneme, that corresponds to aportion of a word. Collectively, the constituent phonemes for a wordrepresent the phonetic spelling of the word. Acoustic models also mayrepresent silence and various types of environmental noise. In general,the processor may identify text that corresponds to the best-matchingacoustic models by reference to phonetic word models in an activevocabulary of words and phrases.

The words or phrases corresponding to the best matching acoustic modelsare referred to as recognition candidates. The processor may produce asingle recognition candidate for an utterance, or may produce a list ofrecognition candidates.

SUMMARY

In one general aspect, a method of computer-implemented speechrecognition includes performing speech recognition on an utterance toproduce a recognition result for the utterance, and determining if theword closely corresponds to a portion of the phrase. The recognitionresult includes a command, a word, and a phrase. A speech recognitionresult is produced if the word closely corresponds to a portion of thephrase.

Implementations may include one or more of the following features. Forexample, the recognition result may include “

<phrase>

<word>” in the Chinese language. Or, the recognition result may include“Write <word> as in <phrase>” in the English language.

The method may also include extracting the word and the phrase from therecognition result. Determining if the word closely corresponds to aportion of the phrase may include a determining if the word matches asubstring of the phrase. Producing the speech recognition result mayinclude producing the word.

Determining if the word closely corresponds to a portion of the phrasemay include determining if the word sounds similar to a substring of thephrase. Producing the speech recognition result may include producingthe substring of the phrase that sounds similar to the word.

The method may also include producing no speech recognition result ifthe word does not correspond to a portion of the phrase.

The method may also include determining if previously recognized texthas been selected. The method may further include replacing selectedtext with the produced speech recognition result if text has beenselected. Moreover, the method may include inserting the produced speechrecognition result into the text at a predetermined location if text hasnot been selected.

The word-in-phrase command provides a natural way for Chinese users todisambiguate a homophone word by putting it in a larger phrase that isless ambiguous.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a speech recognition system.

FIG. 2 is a block diagram of speech recognition software of the systemof FIG. 1.

FIG. 3 is a flow chart of a signal processing procedure performed by thesoftware of FIG. 2.

FIGS. 4A and 4B are state diagrams of a constraint grammar.

FIG. 5 is a graph of a lexical tree.

FIG. 6 is a graph of a portion of the lexical tree of FIG. 5.

FIG. 7 is a flow chart of a pre-filtering procedure performed by thesoftware of FIG. 2.

FIGS. 8A, 8B and 8C are state graphs representing nodes of the lexicaltree of FIG. 5.

FIGS. 9 and 10 are charts of scores corresponding to the states of thestate graphs of FIGS. 8A, 8B and 8C.

FIG. 11 is a flow chart of a procedure for processing nodes of a lexicaltree.

FIG. 12 is a flow chart of a speech recognition procedure.

FIGS. 13A–N are screen displays of a user interface of the speechrecognition system of FIG. 1.

FIGS. 14 and 15 are flow charts of procedures implemented by the speechrecognition system of FIG. 1.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

A speech recognition system uses a word-in-phrase command in arecognition candidate to disambiguate homophone errors. As backgroundinformation for the discussion of disambiguation of homophones, a speechrecognition system that does not use a word-in-phrase command isdiscussed with reference to FIGS. 1–13N.

Referring to FIG. 1, a speech recognition system 100 includesinput/output (I/O) devices (for example, microphone 102, mouse 104,keyboard 106, and display 108) and a computer 110 having a processor112, an I/O unit 114, and a sound card 116. A memory 118 stores data andprograms such as an operating system 120, an application program 122(for example, a word processing program), and speech recognitionsoftware 124.

A computer suitable for one implementation of the system includes a 700MHz Pentium™ processor, 128 MB memory, 12 GB of available hard drivespace. The computer may run Windows 95, Windows 98, Windows2000, orWindows NT 4.0 as an operating system or any other suitable operatingsystem.

The computer 110 may be used for traditional speech recognition. In thiscase, the microphone 102 receives the user's speech and conveys thespeech, in the form of an analog signal, to the sound card 116, which inturn passes the signal through an analog-to-digital (A/D) converter totransform the analog signal into a set of digital samples. Under controlof the operating system 120 and the speech recognition software 124, theprocessor 112 identifies utterances in the user's continuous speech.Utterances are separated from one another by a pause having asufficiently large, predetermined duration (for example, 160–250milliseconds). Each utterance may include one or more words of theuser's speech.

The system also may include an analog recorder port 126 and/or a digitalrecorder port 128. The analog recorder port 126 is connected to thesound card 116 and is used to transmit speech recorded using an analogor digital hand-held recorder to the sound card. The analog recorderport 126 may be implemented using a line in port. The hand-held recorderis connected to the port using a cable connected between the line inport and a line out or speaker port of the recorder. The analog recorderport 126 may be implemented as a microphone positioned so as to be nextto the speaker of the hand-held recorder when the recorder is insertedinto the port 126, and also may be implemented using the microphone 102.Alternatively, the analog recorder port 126 may be implemented as a tapeplayer that receives a tape recorded using a hand-held recorder andtransmits information recorded on the tape to the sound card 116.

The digital recorder port 128 may be implemented to transfer a digitalfile generated using a hand-held digital recorder 130. This file may betransferred directly into memory 118, or to a storage device such ashard drive 132. The digital recorder port 128 may be implemented as astorage device (for example, a floppy drive or CD-ROM drive) of thecomputer 110.

To implement the speech recognition and processing functions of thesystem 100, the computer 110 runs interface software 140, the speechrecognition software 124, a parser 142, and back-end software 144.Dragon NaturallySpeaking Preferred Edition 3.1, available from DragonSystems, Inc. of Newton, Mass., offers one example of suitable speechrecognition software. The interface software 140 provides a userinterface for controlling the transfer of data from the digital recorderand the generation of action items for use by the back-end software 144.In general, the user interface may be controlled using input devicessuch as a mouse or keyboard, or using voice commands processed by thespeech recognition software 124.

After transferring data from the recorder, the interface software 140provides the digital samples for an action item to the speechrecognition software 124. If the digital samples have been stored usingcompression techniques, the interface software 140 decompresses themprior to providing them to the speech recognition software. In general,the speech recognition software analyzes the digital samples to producea sequence of text, and provides this sequence to the interface software140. The interface software 140 then transfers the text and theassociated time stamp, if any, to the parser 142, which processes thetext in conjunction with the time stamp to generate a parsed version ofthe action item. The parser returns the parsed action item to theinterface software, which displays it to the user. After any editing bythe user, and with user approval, the interface software then transfersthe action item to the appropriate back-end software 144. An example ofback-end software with which the system works is personal informationmanagement software, such as Microsoft Outlook, which is available fromMicrosoft Corporation of Redmond, Wash. Other suitable back-end softwareincludes contact management software, time management software, expensereporting applications, electronic mail programs, and fax programs.

FIG. 2 illustrates components of the speech recognition software 124.For ease of discussion, the following description indicates that thecomponents carry out operations to achieve specified results. However,it should be understood that each component actually causes theprocessor 112 to operate in the specified manner.

Initially, a front end processing module 200 converts the digitalsamples 205 from the sound card 116 (or from the digital recorder port128) into frames of parameters 210 that represent the frequency contentof an utterance. Each frame includes 24 parameters and represents ashort portion (for example, 10 milliseconds) of the utterance.

As shown in FIG. 3, the front end processing module 200 produces a framefrom digital samples according to a procedure 300. The module firstproduces a frequency domain representation X(f) of the portion of theutterance by performing a Fast Fourier Transform (FFT) on the digitalsamples (step 305). Next, the module determines log(X(f))² (step 310).The module may then perform frequency warping (step 315) and a filterbank analysis (step 320) to achieve speaker normalization. See S.Wegmann et al., “Speaker Normalization on Conversational Speech,” Proc.1996 ICASSP, pp. I.339–I.341, which is incorporated by reference.

From the normalized results, the module performs cepstral analysis toproduce twelve cepstral parameters (step 325). The module generates thecepstral parameters by performing an inverse cosine transformation onthe logarithms of the frequency parameters. Cepstral parameters andcepstral differences (described below) have been found to emphasizeinformation important to speech recognition more effectively than do thefrequency parameters. After performing channel normalization of thecepstral parameters (step 330), the module produces twelve cepstraldifferences (that is, the differences between cepstral parameters insuccessive frames) (step 335) and twelve cepstral second differences(that is, the differences between cepstral differences in successiveframes) (step 340). Finally, the module performs an IMELDA linearcombination transformation to select the twenty-four most usefulparameters from the twelve cepstral parameters, the twelve cepstraldifferences, and the twelve cepstral second differences (step 345).

Referring again to FIG. 2, a recognizer 215 receives and processes theframes of an utterance to identify text corresponding to the utterance.The recognizer entertains several hypotheses about the text andassociates a score with each hypothesis. The score reflects theprobability that a hypothesis corresponds to the user's speech. For easeof processing, scores are maintained as negative logarithmic values.Accordingly, a lower score indicates a better match (a high probability)while a higher score indicates a less likely match (a lowerprobability), with the likelihood of the match decreasing as the scoreincreases. After processing the utterance, the recognizer provides thebest-scoring hypotheses to the control/interface module 220 as a list ofrecognition candidates, where each recognition candidate corresponds toa hypothesis and has an associated score. Some recognition candidatesmay correspond to text while other recognition candidates correspond tocommands. Commands may include words, phrases, or sentences.

The recognizer 215 processes the frames 210 of an utterance in view ofone or more constraint grammars 225. A constraint grammar, also referredto as a template or restriction rule, may be a limitation on the wordsthat may correspond to an utterance, a limitation on the order orgrammatical form of the words, or both. For example, a constraintgrammar for menu-manipulation commands may include only entries from themenu (for example, “file”, “edit”) or command words for navigatingthrough the menu (for example, “up”, “down”, “top”, “bottom”). Differentconstraint grammars may be active at different times. For example, aconstraint grammar may be associated with a particular applicationprogram 122 and may be activated when the user opens the applicationprogram and deactivated when the user closes the application program.The recognizer 215 discards any hypothesis that does not comply with anactive constraint grammar. In addition, the recognizer 215 may adjustthe score of a hypothesis associated with a particular constraintgrammar based on characteristics of the constraint grammar.

FIG. 4A illustrates an example of a constraint grammar for a “select”command used to select previously recognized text. As shown, aconstraint grammar may be illustrated as a state diagram 400. The“select” command includes the word “select” followed by one or morepreviously-recognized words, with the words being in the order in whichthey were previously recognized. The first state 405 of the constraintgrammar indicates that the first word of the select command must be“select”. After the word “select”, the constraint grammar permits atransition along a path 410 to a second state 415 that requires the nextword in the command to be a previously-recognized word. A path 420,which returns to the second state 415, indicates that the command mayinclude additional previously-recognized words. A path 425, which exitsthe second state 415 and completes the command, indicates that thecommand may include only previously-recognized words. FIG. 4Billustrates the state diagram 450 of the constraint grammar for theselect command when a previously-recognized utterance is “four score andseven”. This state diagram could be expanded to include words fromadditional utterances. The “select” command and techniques forgenerating its constraint grammar are described further in U.S. Pat. No.5,794,189, titled “CONTINUOUS SPEECH RECOGNITION” and issued Aug. 11,1998, which is incorporated by reference.

The constraint grammar also may be expressed in Backus-Naur Form (BNF)or Extended BNF (EBNF). In EBNF, the grammar for the “Select” commandis:

<recognition result> ::= Select <words>, where <words> ::=[PRW¹[PRW²[PRW³ . . . PRW^(n)]]] | [PRW²[PRW³ . . . PRW^(n)]] | . . .[PRW^(n)], “PRW^(i)” is the previously-recognized word i, [ ] meansoptional, < > means a rule, | means an OR function, and ::= means “isdefined as” or “is”.

As illustrated in FIGS. 4A and 4B, this notation indicates that “select”may be followed by any ordered sequence of previously-recognized words.This grammar does not permit optional or alternate words. In someinstances, the grammar may be modified to permit optional words (forexample, an optional “and” to permit “four score and seven” or “fourscore seven”) or alternate words or phrases (for example, “four scoreand seven” or “eighty seven”). Constraint grammars are discussed furtherin U.S. Pat. No. 5,799,279, titled “CONTINUOUS RECOGNITION OF SPEECH ANDCOMMANDS” and issued Aug. 25, 1998, which is incorporated by reference.

Another constraint grammar 225 that may be used by the speechrecognition software 124 is a large vocabulary dictation grammar. Thelarge vocabulary dictation grammar identifies words included in theactive vocabulary 230, which is the vocabulary of words known to thesoftware. The large vocabulary dictation grammar also indicates thefrequency with which words occur. A language model associated with thelarge vocabulary dictation grammar may be a unigram model that indicatesthe frequency with which a word occurs independently of context, or abigram model that indicates the frequency with which a word occurs inthe context of a preceding word. For example, a bigram model mayindicate that a noun or adjective is more likely to follow the word“the” than is a verb or preposition.

Other constraint grammars 225 include an in-line dictation macrosgrammar for dictation commands, such as “CAP” or “Capitalize” tocapitalize a word and “New-Paragraph” to start a new paragraph; theselect X Y Z grammar discussed above and used in selecting text; anerror correction commands grammar; a dictation editing grammar; anapplication command and control grammar that may be used to control aparticular application program 122; a global command and control grammarthat may be used to control the operating system 120 and the speechrecognition software 124; a menu and dialog tracking grammar that may beused to manipulate menus; and a keyboard control grammar that permitsthe use of speech in place of input devices, such as the keyboard 106 orthe mouse 104.

The active vocabulary 230 uses a pronunciation model in which each wordis represented by a series of phonemes that make up the phoneticspelling of the word. Each phoneme may be represented as a triphone thatincludes three nodes. A triphone is a context-dependent phoneme. Forexample, the triphone “abc” represents the phoneme “b” in the context ofthe phonemes “a” and “c”, with the phoneme “b” being preceded by thephoneme “a” and followed by the phoneme “c”.

One or more vocabulary files may be associated with each user. Thevocabulary files contain all of the words, pronunciations, and languagemodel information for the user. Dictation and command grammars may besplit between vocabulary files to optimize language model informationand memory use, and to keep each single vocabulary file under a sizelimit, for example, 64,000 words. There also is a set of systemvocabularies.

Separate acoustic models 235 are provided for each user of the system.Initially, speaker-independent acoustic models of male or female speechare adapted to a particular user's speech using an enrollment program.The acoustic models may be further adapted as the system is used. Theacoustic models are maintained in a file separate from the activevocabulary 230.

The acoustic models 235 represent phonemes. In the case of triphones,the acoustic models 235 represent each triphone node as a mixture ofGaussian probability density functions (“PDFs”). For example, node “i”of a triphone “abc” may be represented as ab^(i)c:

${{{ab}^{i}c} = {\sum\limits_{k}^{\;}{w_{k}{N\left( {\mu_{k},c_{k}} \right)}}}},$where each w_(k) is a mixture weight,

${{\sum\limits_{k}^{\;}w_{k}} = 1},$μ_(k) is a mean vector for the probability density function (“PDF”)N_(k), and c_(k) is the covariance matrix for the PDF N_(k). Like theframes in the sequence of frames, the vectors μ_(k) each include twentyfour parameters. The matrices c_(k) are twenty four by twenty fourmatrices. Each triphone node may be represented as a mixture of up to,for example, sixteen different PDFs.

A particular PDF may be used in the representation of multiple triphonenodes. Accordingly, the acoustic models 235 represent each triphone nodeas a collection of mixture weights w_(k) associated with up to sixteendifferent PDFs N_(k) and separately represent each PDF N_(k) using amean vector μ_(k) and a covariance matrix c_(k). Use of a particular PDFto represent multiple triphone nodes permits the models to include asmaller number of PDFs than would be required if each triphone nodeincluded entirely separate PDFs. Since the English language may beroughly represented using 43 different phonemes, there may be up to79,507 (433) different triphones, which would result in a huge number ofPDFs if each triphone node were represented by a separate set of PDFs.Representing multiple nodes with common PDFs also may remedy or reduce adata sparsity problem that results because some triphones (for example,“tzp” in the English language) rarely occur. These rare triphones may berepresented by having closely-related triphones share the same set ofPDFs.

A large vocabulary dictation grammar may include multiple dictationtopics (for example, “medical” or “legal”), each having its ownvocabulary file and its own language model. A dictation topic includes aset of words that represents the active vocabulary 230. In a typicalexample, a topic may include approximately 30,000 words that areconsidered for normal recognition.

A complete dictation vocabulary consists of the active vocabulary 230plus a backup vocabulary 245. The backup vocabulary may include filesthat contain user-specific backup vocabulary words and system-widebackup vocabulary words.

User-specific backup vocabulary words include words that a user hascreated while using the speech recognition software. These words arestored in vocabulary files for the user and for the dictation topic, andare available as part of the backup dictionary for the dictation topicregardless of user, and to the user regardless of which dictation topicis being used. For example, if a user is using a medical topic and addsthe word “ganglion” to the dictation vocabulary, any other user of themedical topic will have immediate access to the word “ganglion”. Inaddition, the word will be written into the user-specific backupvocabulary. Then, if the user says “ganglion” while using a legal topic,the word “ganglion” will be available during correction from the backupdictionary.

In addition to the user-specific backup vocabulary noted above, there isa system-wide backup vocabulary. The system-wide backup vocabularycontains all the words known to the system, including words that maycurrently be in an active vocabulary.

Referring again to FIG. 2, the recognizer 215 may operate in parallelwith a pre-filtering procedure 240. Upon initiating processing of anutterance, the recognizer 215 requests from the pre-filtering procedure240 a list of words that may have been spoken as the first word of theutterance (that is, words that may correspond to the first andsubsequent frames of the utterance). The pre-filtering procedure 240performs a coarse comparison of the sequence of frames with the activevocabulary 230 to identify a subset of the vocabulary for which a moreextensive comparison using the recognizer is justified.

Referring to FIGS. 5 and 6, the pre-filtering procedure 240 uses alexical tree 500 that is initialized before processing begins. Thelexical tree represents the active vocabulary 230 based on the phoneticrelationships between words in the vocabulary. The lexical tree includesa root node 505 that represents new words entering the lexical tree.From the root node 505, the tree expands to a group 510 of nodes thatcorrespond to phonemes with which words start. A silence node 512 thatrepresents silence also may be reached from the root node 505.

Each node in the group 510 represents a phoneme that appears at thebeginning of one or more words. For example, in the portion 600 of thelexical tree 500 illustrated in FIG. 6, a node 610 corresponds to allwords in the vocabulary that start with the phoneme “H”. Together, thenodes in the group 510 include representations of the starting phonemeof every word in the vocabulary.

The lexical tree continues to expand until it reaches leaf nodes 515that represent the actual words of the vocabulary. For example, asindicated by the square marker, leaf node 615 of FIG. 6 corresponds tothe word “healing”. An internal node of the tree also may represent aword of the vocabulary. For example, the node 520 might represent aparticular vocabulary word in addition to representing the first twophonemes of other vocabulary words. Similarly, the leaf node 620 of FIG.6 corresponds to the words “heal” and “heel” while also corresponding tothe first three phonemes of the words “heals”, “heels” and “healing”.Node 620 also illustrates that, since multiple words may have the samephonetic spelling, a leaf node may correspond to more than one word. Asillustrated in FIG. 6, leaf nodes may appear at different levels withinthe lexical tree. Leaf nodes also may correspond to commands. Forexample, a leaf node may correspond to the word “select” and to thecommand “SELECT”. As noted above, commands may be associated withparticular constraint grammars 225.

Operation of the pre-filtering procedure 240 is illustrated in FIG. 7.The pre-filtering procedure begins by retrieving the next frame ofparameters for an utterance (step 700). Immediately afterinitialization, the next frame will be the first frame for theutterance. Thereafter, the next frame will be the frame following thelast frame that was processed by the pre-filtering procedure when thepre-filtering procedure was last called. The pre-filtering proceduredoes not reinitialize the lexical tree between requests for list ofwords. Accordingly, the state of the lexical tree when a list of wordsis requested corresponds to the state of the lexical tree after aprevious list of words was returned.

After retrieving a frame of data, the pre-filtering procedure finds anactive node in the tree with no unprocessed active successors (step705). Successors of a node also may be referred to as subnodes of thenode. When the lexical tree is initialized, the silence node 512 is theonly active node.

Next, the pre-filtering procedure processes the current node (step 710)according to a node-processing procedure 1100 that is discussed belowwith reference to FIG. 11. The node-processing procedure determineswhether the node should spawn additional active nodes and whether thenode should be rendered inactive. If the node is a leaf node, thenode-processing procedure also determines whether the word correspondingto the node should be added to a word list for a time associated withthe node.

After processing the node (step 710), the pre-filtering proceduredetermines whether the node is the highest node in the tree (that is,the root node) (step 715). If the node is not the highest node, then thepre-filtering procedure goes to the next node having no unprocessedactive subnodes (step 720) and processes that node (step 710). Whensearching for the next node to process, the pre-filtering procedureconsiders inactive nodes having active subnodes or active siblings.

If the processed node is the highest active node (step 715), then thepre-filtering procedure processes the silence node 512 (step 725). Ingeneral, the silence node is processed by comparing a frame to a modelfor silence and adding the resulting score to the minimum of the currentscore for the silence node and the score for the root node 505.

Next, the pre-filtering procedure reseeds the lexical tree (step 730).The pre-filtering procedure reseeds the tree whenever the silence node512 is active or a word was produced by a leaf node of the lexical tree,regardless of whether the word was added to the list of words. Thepre-filtering procedure reseeds the tree by replacing the score for theroot node 505 with the minimum of the score for the silence node 512 andthe scores for any words produced by leaf nodes of the lexical tree forthe current frame. If the silence node is inactive and no leaf node hasproduced a word, then the pre-filtering procedure replaces the score forthe root node 505 with a bad score (that is, a score having a valuelarger than a pruning threshold).

Next, the pre-filtering procedure determines whether more words may beadded to the word list for the requested time (step 735). If there areno active nodes in the lexical tree corresponding to speech that startedat, before, or slightly after the start time for which the list wasrequested, and if the last frame to be processed corresponds to a timethat is slightly a after the start time for which the list wasrequested, then no more words may be added to the word list. A wordproduced by the lexical tree is added to the list of words correspondingto the start time of the word and to lists of words corresponding totimes that precede and follow the start time of the word. It is for thisreason that the pre-filtering procedure waits until there are no activenodes in the tree corresponding to speech that started slightly afterthe start time for the list of words. If more words may be added, thenthe pre-filtering procedure retrieves the next frame of parameters (step700) and repeats the steps discussed above.

If words cannot be added to the word list (step 735), then thepre-filtering procedure returns the word list (step 740) to therecognizer 215. If the word list includes more than a predefined numberof words, then the pre-filtering procedure removes words from the listprior to returning the list. The pre-filtering procedure removes thewords that are least likely to correspond to the user's speech andremoves enough words to reduce the number of words on the list to thepredefined number. The procedure also deletes any lists of words fortimes prior to the requested start time.

Each node of the lexical tree 500 (FIG. 5) represents a sequence ofstates for a particular phoneme. For example, FIG. 8A illustrates a node800 that includes a first state 805, a second state 810, and a thirdstate 815. A comparison with a frame of parameters may cause the scorein a particular state to remain in the state (through a path 820). Ascore remains in the state when the score, after being adjusted based ona comparison with a model for the state, is better than a score passedfrom a preceding state or node, or when no score is passed from apreceding state or node. The comparison also may cause the score to bepassed to a subsequent state through a path 825. A score is passed to asubsequent state when the score, after being adjusted based on acomparison with a model for the subsequent state, is better than thescore in the subsequent state, or when no score is associated with thesubsequent state. The score for the third state 815 may be passed to oneor more subsequent nodes through a path 830.

Referring to FIG. 8B, the node 512 that corresponds to silence isrepresented by a single state 840. Each comparison with a frame ofparameters may cause a score in the node to remain in the state 840(through the path 845) and also may cause the score to be passed to theroot node 505 through a path 850.

Referring to FIG. 8C, the root node 505 is represented by a single state860. Comparison with a frame causes the score in the node to be passedto one or more subsequent nodes (including the silence node 512) througha path 865.

Each state of a node may be represented by four values: a score, astarting time, a leaving penalty, and a staying penalty. The scorerepresents the likelihood that a series of frames has placed the lexicaltree in the state (that is, the probability that the series of framescorresponds to the word or portion of a word to which the statecorresponds). The scores are maintained as negative logarithmic values.

The starting time identifies the hypothesized time at which the userbegan to speak the word or words represented by the state. Inparticular, the starting time identifies the time at which the scoreassociated with the state entered the lexical tree (that is, the time atwhich the score was passed from the state 840 along the path 850).

The leaving and staying penalties are fixed values associated with thestate. The staying penalty is added to any score that stays in thestate. The staying penalty is related inversely to the length of thesound represented by the state and to the length of the phonemerepresented by the node to which the state belongs. For example, thestaying penalty could be proportional to −log(1−1/d_(avg)), whered_(avg) is the average duration, in frames, of the sound represented bythe state. Thus, the staying penalty has a relatively large value whenthe sound corresponding to the state occurs for only a small amount oftime and a relatively small value when the sound corresponding to thestate occurs for a large amount of time.

The leaving penalty is added to any score that exits the state, andincludes a duration component and a language model component. Theduration component is related directly to the length of the soundrepresented by the state and to the length of the phoneme represented bythe node to which the state belongs. For example, the duration componentof the leaving penalty could be proportional to −log(1/d_(avg)). Thus,the duration component of the leaving penalty has a relatively largevalue when the sound corresponding to the state occurs for a largeamount of time and a relatively small value when the sound correspondingto the state occurs for a small amount of time.

The language model components of the leaving penalties for all states ina particular node together represent a language model score for thephoneme associated with that node. The language model score representsthe likelihood that a word including the phoneme will occur in speech.The language model score included in the leaving penalties for a node isthe increase in the best language model score for the branch of thelexical tree that begins with the node relative to the branch of thelexical tree that begins with the node's parent.

The following discussion assumes that there are no leaving or stayingpenalties associated with the state 840 or the state 860. The sameresult could be achieved by setting the leaving and staying penaltiesfor states 840 and 860 equal to zero. The following discussion alsoassumes that the first frame is the first frame that may correspond tospeech instead of silence.

FIG. 9 provides a simplified example of how scores propagate through thelexical tree. Before the first frame is retrieved (row 900), state 840(which corresponds to silence) has a score of 0 and no other nodes areactive. The score of 0 means that there is a one hundred percentprobability that the system is starting from silence.

After the first frame is retrieved (row 905), the score for the state840 (S_(A1)) is set equal to the acoustic score (A_(A1)) resulting froman acoustic match of the first frame with an acoustic modelcorresponding to the state 840 (that is, the acoustic model forsilence). Thus, the score for the state 840 (S_(A1)) is set equal to thelikelihood that the first frame corresponds to silence.

Retrieval of the first frame also causes the state 805 to become anactive state. Assuming that the node 800 corresponds to a phoneme thatstarts a word, the score for the state 805 (S_(B1)) is set equal to theacoustic score (A_(B1)) resulting from an acoustic match of the firstframe with the acoustic model corresponding to the state 805. Thus, thescore for the state 805 (S_(B1)) is set equal to the likelihood that thefirst frame corresponds to the state 805. The starting time for thestate 805 is set equal the time associated with the first frame. Thisvalue for the starting time indicates that the score at state 805represents a word that started at a time corresponding to the firstframe. The starting time moves with the score as the score propagatesthrough the lexical tree.

After the second frame is retrieved (row 910), the score for the state840 (S_(A2)) is set equal to the sum of the previous score for the state(S_(A1)) and the acoustic score (A_(A2)) resulting from an acousticmatch of the second frame with the acoustic model for silence:S _(A2) =S _(A1) +A _(A2) =A _(A1) +A _(A2).As noted above, each of the scores corresponds to a negative logarithmicprobability. Accordingly, adding scores together corresponds tomultiplying the probabilities. Thus, the score for the state 840(S_(A2)) equals the likelihood that both of the first and second framescorrespond to silence. This process is repeated for subsequent frames(for example, lines 915 and 920) so that the score for the state 840 ata frame “n” (S_(An)) equals:

$S_{An} = {{S_{{An} - 1} + A_{An}} = {\sum\limits_{m = 1}^{n}{A_{Am}.}}}$This expression assumes that the silence node 512 is not reseeded fromthe root node 505. If reseeding occurs at a frame n, then the value ofS_(An-1) would be replaced by the score in the root node 505 for theframe n-1.

After the second frame is retrieved, the score for the state 805(S_(B2)) is set equal to:S _(B2)=min(S _(B1) +stay _(B) , S _(A1))+A _(B2),where A_(B2) is the acoustic score resulting from an acoustic match ofthe second frame with the acoustic model corresponding to state 805 andstay_(B) is the staying penalty for state 805. The score for state 805(S_(B2)) corresponds to the more likely of two alternatives: (1) thefirst frame was silence and the second frame was the sound representedby the state 805 or (2) both of the first and second frames were thesound represented by the state 805. The first alternative corresponds toa transition from state 840 to state 805 along the path 850. The secondalternative corresponds to a transition from state 805 back to state 805along path 820. When the first alternative is the more likely, thestarting time corresponding to the first frame that was storedpreviously for the state 805 is replaced by a value corresponding to thesecond frame. This value indicates that the score at state 805represents a word that started with the second frame.

After the second frame is retrieved, the state 810 becomes an activestate. The score for the state 810 (S_(C2)) is set equal to:S _(C2) =S _(B1) +leave _(B) +A _(C2),where A_(C2) is the acoustic score resulting from an acoustic match ofthe second frame with the acoustic model corresponding to state 810 andleave_(B) is the leaving penalty for the state 805. Similarly, leave_(C)and leave_(D) are leaving penalties for, respectively, states 810 and815. The sum of language model components of leave_(B), leave_(C) andleave_(D) represents the language model score for the phonemerepresented by the node 800.

The methodology for determining state scores for states other than thesilence state can be expressed more generally as:S _(i,j)=min(S _(i,j-1) +stay _(i) ,S _(i-1,j-1) +leave _(j-1))+A_(i,j),for i greater than zero (where i equals zero corresponds to silence),and with the boundary condition that the score for an inactive stateequals infinity or some sufficiently large value. The starting time forthe state may be represented as:t _(i,j) =t _(i,j-1) for S _(i,j-1) +stay _(i) ≦S _(i-1,j-1) +leave_(j-1),ort_(i,j) =t _(i-1,j-1) for S _(i,j-1) +stay _(i) >S _(i-1,j-1) +leave_(j-1)for i and j greater than zero and with the boundary condition that thetime value for a newly active state represents the frame at which thestate became active. As previously noted, state scores for the silencestate may be determined as:

$S_{0,j} = {{S_{0,{j - 1}} + A_{0,j}} = {\sum\limits_{m = 1}^{j}A_{0,m}}}$with the boundary condition that S_(0,0) equals zero. An even moregeneral form, in which the scores are expressed as functions of thevarious parameters, is illustrated in FIG. 10.

Referring to FIG. 11, a node may be processed according to anode-processing procedure 1100. Initially, the node-processing procedureupdates the scores and time values for each state of the node (step1105). The node-processing procedure updates the scores and time valuesby generating acoustic scores and using the equations discussed above.

When the last state of the node was active prior to updating the scoresfor the node, the node-processing procedure uses the score for the laststate to generate scores for any inactive subnodes of the node. If thegenerated score for a subnode does not exceed a pruning threshold, thenthe node-processing procedure activates that subnode and provides thesubnode with the generated score.

Next, the node-processing procedure determines whether the score of anystate of the node exceeds the pruning threshold (step 1110). When ascore exceeds the pruning threshold, the likelihood that the wordrepresented by the score was spoken is deemed to be too small to meritfurther consideration. For this reason, the procedure prunes the lexicaltree by deactivating any state having a score that exceeds the pruningthreshold (step 1115). If every state of the node is deactivated, thenthe node-processing procedure also deactivates the node. Thenode-processing procedure may deactivate a node or state by deleting arecord associated with the node or state, or by indicating in the recordthat the node or state is inactive. Similarly, the node-processingprocedure may activate a node or state by creating a record andassociating the record with the node or state, or by indicating in anexisting record that the node or state is active. The procedure may usea dynamic pruning threshold that accounts for variations in the averageor best score in the lexical tree at any given time.

Next, the node-processing procedure determines whether a word is to beadded to a list of words (step 1120). A word is added to the list ofwords when the node being processed corresponds to the last phoneme of aword, a score has been propagated out of the last state of the node, andthe score is less than a list threshold. Before comparing the score tothe list threshold, the node-processing procedure adds a language modelscore to the score. The language model score corresponds to thedifference between the language model score for the word and theincremental language model score that is already included in the score.In general, the list threshold has a lower value than the pruningthreshold. If the node being processed corresponds to the last phonemeof multiple words having the same phonetic spelling, then all of thewords to which the node corresponds are added to the list of words.

If the noted conditions are met, the node-processing procedure adds theword or words to the list (step 1125). A word is stored in the list ofwords along with the score propagated out of the last state. If the wordis on the list already, then the node-processing procedure stores withthe list the better of the score already stored with the list or thescore propagated out of the last state. The scores for words in a listof words are returned along with the list of words. The recognizer 215uses these scores in making the detailed match.

The node-processing procedure also adds the word to lists of words fortimes that precede or follow the starting time to account for possibleinaccuracies in the starting time of the word that may result fromselecting the better of a score that remains in a state or a scorepropagated from a prior state. Spreading the word across multiple listsensures that these inaccuracies will not impinge on the accuracy of thespeech recognition system. The node-processing procedure spreads theword across multiple lists based on the length of the word.

After adding a word to the list of words (step 1125), thenode-processing procedure saves the score associated with the word as areseeding score for use in reseeding the tree (step 1130). Production ofa word by the lexical tree means that the current frame may correspondto the last frame of the word (with the probability of such acorrespondence being reflected by the score associated with the word).This means that the next frame may correspond to the beginning of a wordor to silence resulting from a pause between words. The pre-filteringprocedure reseeds the tree (step 730 of FIG. 7) to account for thispossibility.

For a given frame, multiple nodes may produce words. However, the treeonly needs to be reseeded once. To account for this, the node-processingprocedure only saves the score associated with a word (S_(w)) as thereseeding score (S_(RS)) if the word is the first word to be generatedby the tree for the current frame of if the word score is less than thescore for all other words generated by previously-processed nodes forthe current frame (S_(RS)′):S _(RS)=min(S _(W) , S _(RS)′).Saving only the lowest score (that is, the score indicating the highestprobability that the current frame was the last frame of a word) ensuresthat the tree will be reseeded using the highest probability that thenext frame is the first frame of a new word.

To reseed the tree (step 730 of FIG. 7), the pre-filtering procedureactivates the root node 505 and associates the minimum of the reseedingscore (S_(RS)) and the score for the silence node 512 with the rootnode. During processing of the next frame, the active root node 505 maybe used to activate nodes in the group 510 or to activate the silencenode 512.

Processing of the node is complete after the node-processing proceduresaves a score for use in reseeding the tree (step 1130), or if no wordis to be added to the list of words (step 1120). The lexical treepre-filtering procedure is discussed in detail in U.S. Pat. No.5,822,730, titled “LEXICAL TREE PRE-FILTERING IN SPEECH RECOGNITION” andissued Oct. 13, 1998, which is incorporated by reference.

After the pre-filtering procedure responds with the requested list ofwords, the recognizer initiates a hypothesis for each word from the listand compares acoustic models for the word to the frames of parametersrepresenting the utterance. The recognizer uses the results of thesecomparisons to generate scores for the hypotheses. Hypotheses havingexcessive scores are eliminated from further consideration. As notedabove, hypotheses that comply with no active constraint grammar also areeliminated.

When the recognizer determines that a word of a hypothesis has ended,the recognizer requests from the pre-filtering procedure a list of wordsthat may have been spoken just after the ending-time of the word. Therecognizer then generates a new hypothesis for each word on the list,where each new hypothesis includes the words of the old hypothesis plusthe corresponding new word from the list.

In generating the score for a hypothesis, the recognizer uses acousticscores for words of the hypothesis, a language model score thatindicates the likelihood that words of the hypothesis are used together,and scores provided for each word of the hypothesis by the pre-filteringprocedure. The scores provided by the pre-filtering procedure includecomponents corresponding to a crude acoustic comparison and a languagemodel score indicative of the likelihood that a word is used,independently of context. The recognizer may eliminate any hypothesisthat is associated with a constraint grammar (for example, a commandhypothesis), but does not comply with the constraint grammar.

Referring to FIG. 12, the recognizer 215 operates according to aprocedure 1200. First, prior to processing, the recognizer 215initializes the lexical tree 500 as described above (step 1205). Therecognizer 215 then retrieves a frame of parameters (step 1210) anddetermines whether there are hypotheses to be considered for the frame(step 1215). The first frame always corresponds to silence so that thereare no hypotheses to be considered for the first frame.

If hypotheses need to be considered for the frame (step 1215), therecognizer 215 goes to the first hypothesis (step 1220). The recognizerthen compares the frame to acoustic models 235 for the last word of thehypothesis (step 1225) and, based on the comparison, updates a scoreassociated with the hypothesis (step 1230).

After updating the score (step 1230), the recognizer determines whetherthe user was likely to have spoken the word or words corresponding tothe hypothesis (step 1235). The recognizer makes this determination bycomparing the current score for the hypothesis to a threshold value. Ifthe score exceeds the threshold value, then the recognizer 215determines that the hypothesis is too unlikely to merit furtherconsideration and deletes the hypothesis (step 1240).

If the recognizer determines that the word or words corresponding to thehypothesis were likely to have been spoken by the user, then therecognizer determines whether the last word of the hypothesis is ending(step 1245). The recognizer determines that a word is ending when theframe corresponds to the last component of the model for the word. Ifthe recognizer determines that a word is ending (step 1245), therecognizer sets a flag that indicates that the next frame may correspondto the beginning of a word (step 1250).

If there are additional hypotheses to be considered for the frame (step1255), then the recognizer selects the next hypothesis (step 1260) andrepeats the comparison (step 1225) and other steps. If there are no morehypotheses to be considered for the frame (step 1255), then therecognizer determines whether there are more frames to be considered forthe utterance (step 1265). The recognizer determines that there are moreframes to be considered when two conditions are met. First, more framesmust be available. Second, the best-scoring node for the current frameor for one or more of a predetermined number of immediately precedingframes must have been a node other than the silence node (that is, theutterance has ended when the silence node is the best-scoring node forthe current frame and for a predetermined number of consecutivepreceding frames).

If there are more frames to be considered (step 1265) and the flagindicating that a word has ended is set (step 1270), or if there were nohypotheses to be considered for the frame (step 1215), then therecognizer requests from the pre-filtering procedure 240 a list of wordsthat may start with the next frame (step 1275).

Upon receiving the list of words from the pre-filtering procedure, therecognizer uses the list of words to create hypotheses or to expand anyhypothesis for which a word has ended (step 1280). Each word in the listof words has an associated score. Prior to adding a list word to ahypothesis, the recognizer modifies the list score (S_(L)) for the wordto produce a modified list score (S_(ML)) as:S _(ML) =S _(L) +L _(C) −L _(L),where L_(C) is a language model score that represents the frequency withwhich the pair of words that includes the list word and the immediatelypreceding word in the hypothesis are used together in speech, and L_(L)is a language model score included in the list score and corresponds tothe frequency with which the list word is used in speech, withoutreference to context. The recognizer then adds the modified list scoreto the score for the hypothesis and compares the result to a thresholdvalue. If the result is less than the threshold value, then therecognizer maintains the hypothesis. Otherwise, the recognizerdetermines that the hypothesis does not merit further consideration andabandons the hypothesis. As an additional part of creating or expandingthe hypotheses, the recognizer compares the hypotheses to the activeconstraint grammars 225 and abandons any hypothesis that corresponds tono active constraint grammar. The recognizer then retrieves the nextframe (step 1210) and repeats the procedure.

If there are no more speech frames to process, then the recognizer 215provides the most likely hypotheses to the control/interface module 220as recognition candidates (step 1285).

The control/interface module 220 controls operation of the speechrecognition software and provides an interface to other software or tothe user. The control/interface module receives the list of recognitioncandidates for each utterance from the recognizer. Recognitioncandidates may correspond to dictated text, speech recognition commands,or external commands. When the best-scoring recognition candidatecorresponds to dictated text, the control/interface module provides thetext to an active application, such as a word processor. Thecontrol/interface module also may display the best-scoring recognitioncandidate to the user through a graphical user interface. When thebest-scoring recognition candidate is a command, the control/interfacemodule 220 implements the command. For example, the control/interfacemodule may control operation of the speech recognition software inresponse to speech recognition commands (for example, “wake up”, “makethat”), and may forward external commands to the appropriate software.

The control/interface module also controls the active vocabulary,acoustic models, and constraint grammars that are used by therecognizer. For example, when the speech recognition software is beingused in conjunction with a particular application (for example,Microsoft Word), the control/interface module updates the activevocabulary to include command words associated with that application andactivates constraint grammars associated with the application.

Other functions provided by the control/interface module 220 include anenrollment program, a vocabulary customizer, and a vocabulary manager.The enrollment program collects acoustic information from a user andtrains or adapts a user's models based on that information. Thevocabulary customizer optimizes the language model of a specific topicby scanning user supplied text. The vocabulary manager is a developertool that is used to browse and manipulate vocabularies, grammars andmacros. Each function of the control/interface module 220 may beimplemented as an executable program that is separate from the mainspeech recognition software.

The enrollment program may operate in an interactive mode that guidesthe user through the enrollment process, or in a non-interactive modethat permits the user to enroll independently of the computer. In theinteractive mode, the enrollment program displays the enrollment text tothe user and the user reads the displayed text. As the user reads, therecognizer 215 uses the enrollment grammar to match a sequence ofutterances by the user to sequential portions of the enrollment text.When the recognizer 215 is unsuccessful, the enrollment program promptsthe user to repeat certain passages of the text. The recognizer usesacoustic information from the user's utterances to train or adaptacoustic models 235 corresponding to the matched portions of theenrollment text. The interactive enrollment program is discussed in U.S.application Ser. No. 08/825,536, titled “ENROLLMENT IN SPEECHRECOGNITION” and filed Mar. 28, 1997, now U.S. Pat. No. 6,212,498, whichis incorporated by reference.

When the system makes a recognition error, the user may invoke anappropriate correction command to remedy the error. FIGS. 13A–13Nillustrate a user interface provided by the control/interface module 220in response to a sequence of interspersed text and commands. As shown inFIG. 13A, the recognizer 215 correctly recognizes a first utterance 1300(“When a justice needs a friend New-Paragraph”) and thecontrol/interface module 220 displays the results 1305 (“When a justiceneeds a friend”) of recognizing the utterance in a dictation window1310. The module 220 displays text 1305 (“When a justice needs afriend”) corresponding to a text portion of the utterance and implementsthe formatting command (“New-Paragraph”) included in the utterance.

The recognizer 215 incorrectly recognizes a second utterance 1315(“there are two kinds of legal kibitzers”) by incorrectly recognizingthe word “kibitzers” as “cancers”. The control/interface module 220displays this incorrect result 1316 (“There are two kinds of legalcancers”) in the dictation window 1310. The control/interface modulealso displays the results of recognizing the current utterance, which,in this case, is the second utterance, in a display field 1320 at thebottom of the window 1310.

As shown in FIG. 13B, the user corrects the incorrect recognition byselecting the word “cancers” using the mouse 110 and saying “Spell Thatk i b i”. The control/interface module responds to recognition of the“Spell That” command by displaying a correction dialog box 1325, such asis illustrated in FIG. 13B. The box 1325 displays a numbered list ofwords 1326 starting with the indicated letters (“kibi”). Instead ofusing the mouse 110 to select the word “cancer”, the user could haveverbally selected the word using a “Select” command by saying “Selectcancer”. Similarly, instead of saying “Spell That k i b i”, the usercould have typed the letters “k i b i”.

The user selects the correct word 1327 (“kibitzers”) by saying “Choose4”, where “kibitzers” is the fourth word on the choice list. As shown inFIG. 13C, the control/interface module 220 responds by replacing theincorrect word (“cancers”) with the selected word 1327 in the dictationwindow 1310.

Referring again to FIG. 13B, the correction dialog box 1325 includes a“Train” button 1328. When the user selects this button, thecontrol/interface module responds by prompting the user through atraining session to obtain one or more samples from the user of the wordor words to be trained. The recognizer uses these samples to adaptacoustic models for the words to the user's speech patterns.

As shown in FIG. 13D, the recognizer 215 next misrecognizes a thirdutterance 1329 (“those who pronounce amicus”) and the control/interfacemodule 220 responds by inserting the incorrect text 1330 (“those whoBrown to meet this”) in the dictation window 1310. As shown in FIG. 13E,the user causes the control/interface module 220 to generate thecorrection dialog box 1325 by saying the “Correct That” command 1331.The correction dialog box 1325 includes a list 1332 of recognitioncandidates for the entire utterance 1329. Though the dialog box 1325permits only ten recognition candidates to be displayed at a singletime, the list 1332 may include more than ten entries. Additionalentries may be accessed using a scroll bar 1333.

As shown in FIG. 13F, the user selects the word “Brown” 1335 using themouse 110. As noted above, the user could also select the word “Brown”by using the voice command “Select Brown”. As shown in FIG. 13G, theuser then says “p r o n” 1340 to indicate that the word Brown should bereplaced with a word starting with the letters “pron”. The user couldachieve the same result by typing the letters “pron”. Thecontrol/interface module 220 responds by producing an updated list 1341of recognition candidates, where each recognition candidate includes aword starting with “pron” in the position previously occupied by theword “Brown”. Each of the recognition candidates includes thecorrectly-recognized words that preceded “Brown” (“those who”) and thewords that followed “Brown” (“to meet this”).

As shown in FIG. 13H, the user selects the recognition candidate 1345that includes the word “pronounce” by using the mouse to select thethird entry in the list. The user could achieve the same result bysaying “Choose 3”.

As shown in FIG. 131, the user then uses the mouse to select the words“to meet this” 1350. Then, as shown in FIG. 13J, the user types theletters “amicu”, and the control/interface module 220 responds byproducing an updated list 1351 of recognition candidates that start withthe words “those who pronounce” and include a word starting with theletters “amicu”.

An entry 1352 of the list includes a phrase “amicus curiae” that startswith the letters “amicu”. Since the first entry 1353 is the correctentry, the user clicks on an “OK” button 1354 at the bottom of thecorrection dialog box 1325. As shown in FIG. 13K, the control/interfacemodule 220 responds by placing the correct version 1355 of the utterancein the dictation window 1310. As discussed above, all of the correctionsteps for the utterance are performed within the same correction dialogbox 1325.

As shown in FIG. 13L, the recognizer 215 next misrecognizes an utterance1360 (“Each submits a brief as an outsider”) and the control/interfacemodule 220 responds by inserting the incorrect text 1361 (“Each submitsa brief is an outsider”) in the dictation window 1310. FIG. 13L alsoillustrates another feature of the interface. As an utterance is beingrecognized, the control/interface module 220 may display a partialrecognition candidate 1365 for the utterance. This partial candidaterepresents the best scoring hypotheses for the utterance at a point intime before the recognizer completes processing of the utterance.Display of the partial candidate is useful for long utterances that mayrequire an extended time for processing. As shown in FIG. 13M, the useragain causes the control/interface module 220 to generate the correctiondialog box 1325 by saying the “Correct That” command 1331. Thecorrection dialog box 1325 includes a list 1370 of recognitioncandidates for the entire utterance 1331. Since the text of theutterance 1360 appears as the second entry 1372 on the list 1370, theuser selects the text by saying “Choose 2”. As shown in FIG. 13N, thecontrol/interface module 220 responds by placing the text 1372 in thedialog box 1310.

The various correction procedures are described in U.S. Pat. No.5,794,189, issued Aug. 11, 1998 and U.S. Pat. No. 6,064,959, issued May16, 2000, which are incorporated by reference.

Word-In-Phrase Correction

Homophones are words that sound the same but differ in spelling orrepresentation, origin, and meaning. A word is a sound or a combinationof sounds that symbolizes and communicates a meaning and origin. InEnglish and other European languages, words are represented by one ormore letters from the Roman alphabet.

In the Chinese language, words are typically represented by one or morecharacters from a Chinese syllabary, where each character in thesyllabary is a single syllable. Generally, the one or more charactersthat form a word provide information relating to the meaning andphonetic representation or sound of that word. Characters in the Chinesesyllabary may represent different sounds and the various sounds may berepresented by different characters. Therefore, the speech recognitionsystems described above may fail to correctly distinguish homophones inthe Chinese language.

Although the Chinese language does not have an alphabet, the sounds ofthe Chinese characters may be transcribed or transliterated into theRoman alphabet or another alphabet. For instance, a pinyin systemdeveloped in China and subsequently adopted by the United Nations hasincreasingly become a standard Romanization system worldwide fortranscribing Chinese into the Roman alphabet. In the followingdescription, we adopt the pinyin system of Romanization whentranscribing Chinese characters and include this transcription inparenthesis following the Chinese characters.

Additionally, each word in Chinese has an associated tone, which isrepresented by a number 1, 2, 3, 4, or 5 attached to the end of the wordin the pinyin system.

A word is a pure homophone of another word if the characters of thewords are pronounced exactly the same. For example, both the word

and the word

are pronounced as “hong1.” Therefore,

and

are pure homophones of each other. In the English language, the words-red- and -read- are pure homophones since they may both be pronouncedas “r{hacek over (e)}d.”

A word is a near homophone of another word if the pronunciations of thewords are very close to each other. For example, the word

is pronounced as “shuang1 diao1,” and the word

is pronounced as “shang4 diao4.” Therefore,

and

are near homophones of each other.

Referring to FIG. 14, the speech recognition system may processrecognition results according to a procedure 1400. Initially, the speechrecognition system generates or receives recognition results for anutterance (step 1405). While the recognition results may includemultiple recognition candidates, the system processes only thebest-scoring recognition candidate in implementing the procedure 1400.For ease of discussion, this candidate is referred to as the toprecognition candidate.

As the first step in processing the top recognition candidate, thespeech recognition system determines whether the top recognitioncandidate includes only text (step 1410). If so, the speech recognitionsystem processes the text (step 1415). For example, the speechrecognition system may insert the text into a dictation window.

If the top recognition candidate includes something other than text(step 1410), the speech recognition system determines whether thecandidate includes a word-in-phrase command that permits activedisambiguation of homophones so as to avoid homophone mistakes (step1420). The word-in-phrase command may be implemented in English as, forexample, “Write <word> as in <phrase>.” As an example, the system wouldinsert “Sox” in response to “Write Sox as in Boston Red Sox,” and wouldinsert “socks” in response to “Write socks as in socks for your feet.”In Chinese, the command may be implemented as, for example, “

<phrase>

<word>,” which corresponds to “xie3 zuo4 <phrase> de5 <word>” in Pinyin.

In the word-in-phrase commands, the <word> is a pronunciation of a wordand the <phrase> is any meaningful phrase in which that word iscorrectly used. The keywords “write” and “

” (“xie3 zuo4”) instruct the speech recognition system to interpret thecomplete utterance “write <word> as in <phrase>” or “

<phrase>

<word>” (“xie3 zuo4<Phrase> de5 <word>”) as a command. The keywords arenot required. However, without them, the grammars “<word>as in <phrase>”and “<phrase>

<word>” (“<phrase>de5<word>”) may be too difficult to distinguish fromtext dictation, which could result in “<word>as in <phrase>” or“<phrase>

<word>” being processed as text in step 1415 instead of asword-in-phrase commands.

In generating the top recognition candidate, the speech recognitionsystem determines whether the top recognition candidate corresponds tothe word-in-phrase command “write <word>as in <phrase>” or “

<phrase>

<word>” using a constraint grammar (step 1420). For English, theconstraint grammar requires “write” to be followed by a word <word>, “asin,” and a phrase <phrase>. For Chinese, the constraint grammar requires“

”(“xie3 zuo4”) to be followed by a phrase <phrase>, auxiliary word

(“de5”), and <word>.

If the top recognition candidate includes a command other than theword-in-phrase command (step 1420), the speech recognition systemprocesses the command (step 1425). For example, if the top recognitioncandidate includes text and a formatting command such as New-Paragraph,then the speech recognition system processes the text portion of thecandidate and performs the formatting command. As another example, ifthe top recognition candidate includes the command “make that,” then thespeech recognition system performs the make that correction command.

If the candidate includes a word-in-phrase command (step 1420), then thespeech recognition system processes the word-in-phrase command (step1435) and determines whether a word-in-phrase result has been produced(step 1440). Referring to FIG. 15, the speech recognition systemprocesses the word-in-phrase command according to a procedure 1435.Initially, the speech recognition system extracts the <word> and the<phrase> from the top recognition candidate (step 1500).

After extraction, the system determines if <word> is a substring of<phrase> (step 1505). If <word> is a substring of <phrase>, then thesystem designates <word> as the result of the processing (step 1510).

If <word> is not a substring of <phrase> (step 1505), then the systemdetermines if <word> sounds similar to a word in <phrase> (step 1515).The similarity between the <word> and words in the <phrase> isdetermined using a parameter that may be manually adjusted andfine-tuned by a developer of the speech recognition system. For example,the system may calculate a statistical difference between acousticmodels for different words, and may designate words as being similarwhen this difference is less than a threshold amount. If no word in<phrase> sounds sufficiently similar to <word> (step 1515), then thespeech recognition system produces no word-in-phrase result (step 1520).If the speech recognition system determines that <word> sounds similarto a word in <phrase> (step 1515), then the system designates thatsimilar word from <phrase> as the result of the processing (step 1525).

Referring again to FIG. 14, if a result was not produced in processingthe word-in-phrase command (step 1440), the speech recognition systemtakes no action and awaits further recognition results (step 1405). If aresult was produced (step 1440), the speech recognition systemdetermines if text has been selected (step 1445). Text may be selecteddirectly by the user using any available technique. For example, theuser may highlight text from the dictation window. The user also mayselect text using an input device such as a mouse or keyboard. Inanother implementation, the user may select text using a voice command,such as “select <phrase>.”

If text has been selected (step 1445), the speech recognition systemreplaces the text with the produced result (step 1450). If text has notbeen selected (step 1445), the speech recognition system inserts theproduced result at a predetermined location in the text, such as, forexample, at the current cursor location (step 1455).

For illustrative purposes, several examples of the Chineseword-in-phrase command will now be discussed.

In a first example, if the user speaks “

” (“xie3 zuo4 hong1 wei3 de5 hong1”), the speech recognition systemproduces

(hong1). In this example, <word> is “

” (“hong1”) and <phrase> is “

” (“xie3 zuo4 hong1 wei3”).

As another example, if the user speaks “

” (“xie3 zuo4 yi2 jian4 shuang1 diao1 de5 shang4 diao4”), the speechrecognition system produces

(shuang1 diao1). In this example, <word> is “

” (“shang4 diao4”) and <phrase>is “

” (“xie3 zuo4 yi2 jian4 shuang1 diao1”).

If the user speaks “

” (“xie3 zuo4 mu4 di4 de5 del”), the speech recognition system produces

(di4). In this example, <word> is “

” (“de1”) and <phrase> is “

” (“xie3 zuo4 mu4 di4”).

If the user speaks “

” (“xie3 zuo4 guang1 ming2 de5 ming1 guang1”), the speech recognitionsystem produces no result because neither <word> nor anythingsufficiently similar found in <phrase>. In this example, <word> is “

” (“ming1 guang1”) and <phrase> is “

” (“xie3 zuo4 guang1 ming2”).

Other embodiments are within the scope of the following claims. Forexample, the techniques described here are not limited to any particularhardware or software configuration; they may find applicability in anycomputing or processing environment that may be used for speechrecognition. The techniques may be implemented in hardware or software,or a combination of the two. Preferably, the techniques are implementedin computer programs executing on programmable computers that eachinclude a processor, a storage medium readable by the processor(including volatile and non-volatile memory and/or storage elements), atleast one input device, and at least one output device. Program code isapplied to data entered using the input device to perform the functionsdescribed and to generate output information. The output information isapplied to one or more output devices.

Each program is preferably implemented in a high level procedural orobject oriented programming language to communicate with a computersystem. However, the programs can be implemented in assembly or machinelanguage, if desired. In any case, the language may be a compiled orinterpreted language.

Each such computer program is preferably stored on a storage medium ordevice (for example, CD-ROM, hard disk or magnetic diskette) that isreadable by a general or special purpose programmable computer forconfiguring and operating the computer when the storage medium or deviceis read by the computer to perform the procedures described in thisdocument. The system may also be considered to be implemented as acomputer-readable storage medium, configured with a computer program,where the storage medium so configured causes a computer to operate in aspecific and predefined manner.

The user may be required to pause before and after the word-in-phrasecommand, but not necessarily within the word-in-phrase command.

The speech recognition system may require <word> to be either the firstor the last few characters in <phrase>. The speech recognition systemalso may require <word> to be a single word (that may be one or morecharacters long). The <word> length, which corresponds to the number ofcharacters in the word, may be constrained to be shorter than the lengthof <phrase>. The <phrase> length may include any number of words orcharacters.

The word-in-phrase command may be implemented in any language to corrector avoid recognition errors such as those due to homophone errors. Theword-in-phrase command also may be implemented to correct other types ofmisrecognition results.

Previously recognized text may be automatically selected by the speechrecognition system at step 1445 through use of a “rewrite” command thatmay be implemented as: “Rewrite <word> as in <phrase>.” In this case,the speech recognition system determines that text must be selected forreplacement when “rewrite” is recognized before “<word> as in <phrase>.”The system then responds by selecting an occurrence of <word> or a wordsimilar to <word> from the text. The similar word does not necessarilyneed to be a word included in <phrase>.

The rewrite command may be used to replace a word throughout atranscript when implemented as: “Rewrite all <word> as in <phrase>.” Inthis case, the speech recognition system determines which words in thetranscript should be selected for replacement when “rewrite all” isrecognized before “<word> as in <phrase>.” Once again, the words to bereplaced may match or sound similar to <word>.

1. A method of computer-implemented speech recognition, the methodcomprising: performing speech recognition on an utterance to produce arecognition result for the utterance, the recognition result including acommand, a word, and a phrase; determining if the word is similar to aportion of the phrase; and producing a speech recognition result if theword is similar to a portion of the phrase.
 2. The method of claim 1wherein the recognition result comprises a command in the Chineselanguage.
 3. The method of claim 1 wherein the recognition resultcomprises “Write <word> as in <phrase>” in the English language.
 4. Themethod of claim 1 further comprising extracting the word and the phrasefrom the recognition result.
 5. The method of claim 1 whereindetermining if the word is similar to a portion of the phrase comprisesdetermining if the word matches a substring of the phrase.
 6. The methodof claim 5 wherein producing the speech recognition result comprisesproducing the word.
 7. The method of claim 1 wherein determining if theword is similar to a portion of the phrase comprises determining if theword sounds similar to a substring of the phrase.
 8. The method of claim7 wherein producing the speech recognition result comprises producingthe substring of the phrase that sounds similar to the word.
 9. Themethod of claim 1 further comprising producing no speech recognitionresult if the word is not similar to a portion of the phrase.
 10. Themethod of claim 1 further comprising determining ifpreviously-recognized text has been selected.
 11. The method of claim 10further comprising replacing selected text with the produced speechrecognition result if text has been selected.
 12. The method of claim 11further comprising inserting the produced speech recognition result intothe text at a predetermined location if text has not been selected. 13.A speech recognition system comprising: an input device for receivinguser input; an output device for presenting information to the user; aprocessor having communications links for transmitting information toand from the output and input devices; and memory storing softwareinstructions performed by the processor (i) for performing speechrecognition on an utterance to produce a recognition result for theutterance, the recognition result including a command, a word, and aphrase, (ii) for determining if the word is similar to a portion of thephrase, and (iii) for producing a speech recognition result if the wordis similar to a portion of the phrase.
 14. The system of claim 13wherein the recognition result comprises a command in the Chineselanguage.
 15. The system of claim 13 wherein the recognition resultcomprises “Write <word> as in <phrase>” in the English language.
 16. Thesystem of claim 13 wherein the memory further comprises softwareinstructions for extracting the word and the phrase from the recognitionresult.
 17. The system of claim 13 wherein the software instruction fordetermining if the word is similar to a portion of the phrase comprisesa software instruction for determining if the word matches a substringof the phrase.
 18. The system of claim 17 wherein the softwareinstruction for producing the speech recognition result comprises asoftware instruction for producing the word.
 19. The system of claim 13wherein the software instruction for determining if the word is similarto a portion of the phrase comprises a software instruction fordetermining if the word sounds similar to a substring of the phrase. 20.The system of claim 19 wherein the software instruction for producingthe speech recognition result comprises a software instruction forproducing the substring of the phrase that sounds similar to the word.21. The system of claim 13 wherein the memory further comprises asoftware instruction for producing no speech recognition result if theword is not similar to a portion of the phrase.
 22. The system of claim13 wherein the memory further comprises a software instruction fordetermining if previously-recognized text has been selected.
 23. Thesystem of claim 22 wherein the memory further comprises a softwareinstruction for replacing selected text with the produced speechrecognition result if text has been selected.
 24. The system of claim 23wherein the memory further comprises a software instruction forinserting the produced speech recognition result into the text at apredetermined location if text has not been selected.
 25. Acomputer-readable medium, comprising software instructions for speechrecognition, the software instructions comprising: a first code segmentto perform speech recognition on an utterance to produce a recognitionresult for the utterance, the recognition result including a command, aword, and a phrase; a second code segment to determine if the word issimilar to a portion of the phrase; and a third code segment to producea speech recognition result if the word is similar to a portion of thephrase.
 26. The computer readable medium of claim 25 wherein therecognition result comprises a command in the Chinese language.
 27. Thecomputer readable medium of claim 25 wherein the recognition resultcomprises “Write <word> as in <phrase>” in the English language.
 28. Thecomputer readable medium of claim 25 wherein the software instructionscomprise a fourth code segment to extract the word and the phrase fromthe recognition result.
 29. The computer readable medium of claim 25wherein the second code segment comprises a code segment to determine ifthe word matches a substring of the phrase.
 30. The computer readablemedium of claim 29 wherein the third code segment comprises a codesegment to produce the word.
 31. The computer readable medium of claim25 wherein the second code segment comprises a code segment to determineif the word sounds similar to a substring of the phrase.
 32. Thecomputer readable medium of claim 31 wherein the third code segmentcomprises a code segment to produce the substring of the phrase thatsounds similar to the word.
 33. The computer readable medium of claim 25wherein the software instructions comprise a fourth code segment toproduce no speech recognition result if the word is not similar to aportion of the phrase.
 34. The computer readable medium of claim 25wherein the software instructions comprise a fourth code segment todetermine if previously-recognized text has been selected.
 35. Thecomputer readable medium of claim 34 wherein the software instructionscomprise a fifth code segment to replace selected text with the producedspeech recognition result if text has been selected.
 36. The computerreadable medium of claim 35 wherein the software instructions comprise asixth code segment to insert the produced speech recognition result intothe text at a predetermined location if text has not been selected.